Applied AI ML Lead - Python, LLM & Agentic AI

Salary: £62,000 - 102,000 per year

Requirements:

• We require a Bachelors or Masters degree in Computer Science, Engineering, or a related field.
• We require demonstrated experience in applied AI/ML engineering, with a track record of developing and deploying business-critical machine learning models in production.
• We require proficiency in Python for model development, experimentation, and integration with OpenAI API.
• We require experience with machine learning frameworks, libraries, and APIs such as TensorFlow, PyTorch, Scikit-learn, and OpenAI API.
• We require experience with cloud platforms such as AWS, Azure, or Google Cloud Platform, as well as containerization technologies such as Docker and Kubernetes.
• We require experience with microservices design, implementation, and performance optimization.
• We require a solid understanding of statistics, machine learning fundamentals, and generative model architectures, particularly GANs and VAEs.
• We require the ability to identify and address AI/ML/LLM/GenAI challenges and fine-tune models for optimal performance in NLP applications.
• We require strong collaboration skills and the ability to communicate complex concepts to cross-functional teams.
• We require a portfolio showcasing successful applications of generative models in NLP projects, including examples of using OpenAI APIs for prompt engineering.
• Preferred: familiarity with the financial services industry.
• Preferred: expertise in designing and implementing pipelines using Retrieval-Augmented Generation (RAG).
• Preferred: hands-on knowledge of Chain-of-Thoughts, Tree-of-Thoughts, and Graph-of-Thoughts prompting strategies.

Responsibilities:

• We work closely with product managers, data scientists, ML engineers, and other stakeholders to understand requirements and prioritize use cases.
• We design, develop, and deploy state-of-the-art AI/ML/LLM/GenAI solutions to meet business objectives.
• We manage, mentor, and guide a team of ML and MLOps engineers.
• We develop and maintain automated pipelines for model deployment, ensuring scalability, reliability, and efficiency.
• We implement optimization strategies to fine-tune generative models for specific NLP use cases, ensuring high-quality outputs in summarization and text generation.
• We conduct thorough evaluations of generative models, iterate on model architectures, and implement improvements to enhance performance in NLP applications.
• We implement monitoring mechanisms to track model performance in real time and ensure model reliability.
• We communicate AI/ML/LLM/GenAI capabilities and results to both technical and non-technical audiences.
• We stay informed about the latest trends and advancements in AI/ML/LLM/GenAI research, implement cutting-edge techniques, and leverage external APIs for enhanced functionality.

Technologies:

• AI
• API
• AWS
• Azure
• Cloud
• Docker
• Support
• Kubernetes
• LLM
• Machine Learning
• MLOps
• PyTorch
• Python
• RAG
• TensorFlow
• microservices
• Agentic AI
• Network

More:

We are JPMorgan Chase, a global leader in financial services, providing strategic advice and products to prominent corporations, governments, wealthy individuals, and institutional investors. Our first-class approach to serving clients drives everything we do, and we strive to build trusted, long-term partnerships to help our clients achieve their business objectives. We value our people as our strength and are committed to diversity, inclusion, and equal opportunity. You will join our Corporate Technology team, where we rely on smart, driven people to develop applications and provide tech support across our corporate functions, including Global Finance, Corporate Treasury, Risk Management, Human Resources, Compliance, Legal, and the Corporate Administrative Office. This is a full-time role in our Regulatory, Controls, and Operational Risk Technology (RCORT) team, offering the opportunity to work in a fast-paced, challenging environment and make a direct impact on global business.

last updated 34 week of 2026